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--- |
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license: other |
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language: |
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- en |
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pipeline_tag: text-generation |
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datasets: |
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- LDJnr/Puffin |
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--- |
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# Model Card for Puffin-Phi V2 |
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This is my first fine-tune of Puffin that seems to be working fairly reliably! |
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## Model Details |
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### Model Sources |
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This model was trained on the Puffin Dataset, made by LDJ, using a slightly modified version of the dataset that removed >2000 token entries, so there would be no early cutoffs during training phi, since it's context is 2k. |
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## Uses |
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Let me know! |
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## How to Get Started with the Model |
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Phi does not support device_map "auto", and does not seem to want to inference in fp16, so use bf16. |
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Here is working code to inference, though it can be improved: |
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``` |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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sysprompt = "The assistant gives helpful, detailed, and polite answers to the user's questions.\n" |
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model = AutoModelForCausalLM.from_pretrained("teknium/Puffin-Phi-v2", trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda") |
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tokenizer = AutoTokenizer.from_pretrained("teknium/Puffin-Phi-v2", trust_remote_code=True, torch_dtype=torch.bfloat16) |
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inputs = tokenizer(f"{sysprompt}USER: Write a negative review for the website Twitter.\nASSISTANT:", return_tensors="pt", return_attention_mask=False) |
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outputs = model.generate(**inputs, max_length=128, do_sample=True, temperature=0.2, top_p=0.9, use_cache=True, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id) |
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text = tokenizer.batch_decode(outputs)[0] |
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print(text) |
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``` |
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The prompt format is ShareGPT/Vicuna, so it uses the sysprompt (defualt in sysprompt variable) |
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then is prompted like so: |
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``` |
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USER: <prompt> |
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ASSISTANT: |
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``` |
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## Training Details |
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### Training Procedure |
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Trained with Axolotl. View the wandb runs for all my puffin runs (this is puffin-phi-4 on wandb): |
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https://wandb.ai/teknium1/puffin-phi/runs/puffin-phi-4 |
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## Evaluation |
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TODO |
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